Systems and methods for automatically determining myocardial bridging and patient impact

ABSTRACT

Embodiments include computer-implemented methods and systems for reporting the presence of myocardial bridging in a patient, the method comprising detecting, within a patient-specific model representing at least a portion of the patient&#39;s heart based on patient-specific anatomical image data regarding a geometry of the patient&#39;s heart, a segment of an epicardial coronary artery at least partially surrounded by the patient&#39;s myocardium to determine the presence of myocardial bridging; and computing, using at least one computer processor, at least one physical feature of the myocardial bridging to identify the severity of the myocardial bridging.

PRIORITY

This application is a continuation of U.S. application Ser. No.14/535,755, filed Nov. 7, 2014, which claims the benefit of priority toU.S. Provisional Application No. 62/043,841 filed Aug. 29, 2014, all ofwhich are incorporated herein by reference in their entireties.

TECHNICAL FIELD

Embodiments of the present disclosure include methods and systems formodeling of fluid flow and, more particularly, methods and systems forpatient-specific modeling and evaluation of blood flow.

BACKGROUND

Myocardial bridging is a congenital coronary abnormality in which acoronary segment runs through the myocardium intramurally (e.g., asegment of a coronary artery tunnels through the myocardium instead oflying on top of it), resulting in systolic compression of the tunneledsegment. The frequency of myocardial bridging has been reported to be1.5% to 16% in coronary angiography and as high as 80% in autopsyseries. Myocardial bridging can cause cardiac-related complications suchas ischemia and acute coronary syndromes, and coronary spasms.

Coronary artery disease, in turn, may cause the blood vessels providingblood to the heart to develop lesions, such as a stenosis (abnormalnarrowing of a blood vessel). As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as chronic stable angina duringphysical exertion or unstable angina when the patient is at rest. A moresevere manifestation of disease may lead to myocardial infarction, orheart attack.

Myocardial bridging may occur partially or completely. For example, asegment of a coronary artery of a patient may be completely surroundedby the patient's myocardium (e.g., 100% tunneling of the vessel into themyocardium). Alternatively, the abnormality may occur as partialmyocardial bridging—e.g., 30%-99% of the circumference of a segment of acoronary artery of the patient is surrounded by the myocardium, withtapering and/or reduction of cross-sectional area of the coronaryartery.

Typically, myocardial bridging may be diagnosed by coronary angiographyor intravascular ultrasound imaging (IVUS) based on one or more ofseveral features, including significant percent lumen diameternarrowing, persistent diastolic diameter reduction, a “milking effect”in angiography, and/or a “half moon” phenomenon in IVUS. Besidesmorphological evaluation, intracoronary Doppler may show increased flowvelocity, retrograde systolic flow, and reduced coronary flow reserve inmyocardial bridging. The functional significance of myocardial bridgingmay be evaluated using fractional flow reserve (FFR) with the use ofinotropic agents. FFR may be defined as the ratio of the mean bloodpressure and/or flow downstream of a location, such as a lesion orlocation of myocardial bridging, divided by the mean blood pressureand/or flow upstream from the location, under conditions of increasedcoronary blood flow, e.g., when induced by intravenous administration ofadenosine.

However, traditionally, these methods are invasive procedures and mayinvolve the use of inotropic agents such as dobutamine to induce maximalmyocardial contraction. In some cases, diastolic FFR may be morerelevant than the conventional FFR to the evaluation of myocardialbridging due to overshooting of systolic pressure, which may lead tounderestimation of severity when assessed by the conventional FFR.

As these physiologic and hemodynamic conditions of myocardial bridgingmay hamper the use of conventional invasive FFR, it would be useful todifferentiate patients with fixed stenosis from those with myocardialbridging for an accurate blood flow simulation in assessing thehemodynamic significance of lesions.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosure.

SUMMARY

In accordance with an embodiment, a computer-implemented method forreporting the presence of myocardial bridging in a patient includesreceiving patient-specific anatomical image data regarding a geometry ofthe patient's heart; creating a patient-specific model representing atleast a portion of the patient's heart based on the patient-specificanatomical image data; detecting, within the patient-specific model, asegment of an epicardial coronary artery at least partially surroundedby the patient's myocardium to determine the presence of myocardialbridging; and computing, using at least one computer processor, at leastone physical feature of the myocardial bridging to identify the severityof the myocardial bridging.

In accordance with another embodiment, a system for reporting thepresence of myocardial bridging in a patient includes a data storagedevice storing instructions for reporting the presence of myocardialbridging in a patient; and a processor configured to execute theinstructions to perform a method for reporting the presence ofmyocardial bridging, the method including: receiving patient-specificanatomical image data regarding a geometry of the patient's heart;creating a patient-specific model representing at least a portion of thepatient's heart based on the patient-specific anatomical image data;detecting, within the patient-specific model, a segment of an epicardialcoronary artery at least partially surrounded by the patient'smyocardium to determine the presence of myocardial bridging; andcomputing, using at least one computer processor, at least one physicalfeature of the myocardial bridging to identify the severity of themyocardial bridging.

In accordance with another embodiment, a non-transitory computerreadable medium for use on at least one computer system containscomputer-executable programming instructions for performing a method forreporting the presence of myocardial bridging in a patient, the methodcomprising: receiving patient-specific anatomical image data regarding ageometry of the patient's heart; creating a patient-specific modelrepresenting at least a portion of the patient's heart based on thepatient-specific anatomical image data; detecting, within thepatient-specific model, a segment of an epicardial coronary artery atleast partially surrounded by the patient's myocardium to determine thepresence of myocardial bridging; and computing, using at least onecomputer processor, at least one physical feature of the myocardialbridging to identify the severity of the myocardial bridging.

In accordance with another embodiment, a computer-implemented method forassessing risk and hemodynamic significance of myocardial bridging in apatient includes obtaining a patient-specific model representing atleast a portion of the patient's heart based on patient-specificanatomical image data regarding a geometry of the patient's heart;obtaining at least one estimate of at least one physiological and/orphenotypic parameter of the patient; defining at least one physiologiccondition and/or at least one boundary condition of the patient in aphysiologic stress state using the patient-specific model and the atleast one estimate of at least one physiological and/or phenotypicparameter of the patient; evaluating a degree of myocardial bridging inthe patient by identifying systolic compression of a coronary artery ofthe patient using the patient-specific model; performing computationalfluid dynamics analysis on a myocardial bridging segment under simulatedand/or dobutamine challenge conditions using the degree of myocardialbridging in the patient and the at least one physiologic conditionand/or the at least one boundary condition of the patient; and computingat least one hemodynamic quantity of the myocardial bridging segment toevaluate risk based on the computational fluid dynamics and/orstructural mechanics analysis.

In accordance with another embodiment, a system for assessing risk andhemodynamic significance of myocardial bridging in a patient includes adata storage device storing instructions for reporting the presence ofmyocardial bridging in a patient; and a processor configured to executethe instructions to perform a method including: obtaining apatient-specific model representing at least a portion of the patient'sheart based on patient-specific anatomical image data regarding ageometry of the patient's heart; obtaining at least one estimate of atleast one physiological and/or phenotypic parameter of the patient;defining at least one physiologic condition and/or at least one boundarycondition of the patient in a physiologic stress state using thepatient-specific model and the at least one estimate of at least onephysiological and/or phenotypic parameter of the patient; evaluating adegree of myocardial bridging in the patient by identifying systoliccompression of a coronary artery of the patient using thepatient-specific model; performing computational fluid dynamics analysison a myocardial bridging segment under simulated and/or dobutaminechallenge conditions using the degree of myocardial bridging in thepatient and the at least one physiologic condition and/or the at leastone boundary condition of the patient; and computing at least onehemodynamic quantity of the myocardial bridging segment to evaluate riskbased on the computational fluid dynamics analysis.

In accordance with another embodiment, a non-transitory computerreadable medium for use on at least one computer system containscomputer-executable programming instructions for performing a method forassessing risk and hemodynamic significance of myocardial bridging in apatient, the method including: obtaining a patient-specific modelrepresenting at least a portion of the patient's heart based onpatient-specific anatomical image data regarding a geometry of thepatient's heart; obtaining at least one estimate of at least onephysiological and/or phenotypic parameter of the patient; defining atleast one physiologic condition and/or at least one boundary conditionof the patient in a physiologic stress state using the patient-specificmodel and the at least one estimate of at least one physiological and/orphenotypic parameter of the patient; evaluating a degree of myocardialbridging in the patient by identifying systolic compression of acoronary artery of the patient using the patient-specific model;performing computational fluid dynamics analysis on a myocardialbridging segment under simulated and/or dobutamine challenge conditionsusing the severity of myocardial bridging in the patient and the atleast one physiologic condition and/or the at least one boundarycondition of the patient; and computing at least one hemodynamicquantity of the myocardial bridging segment to evaluate risk based onthe computational fluid dynamics analysis.

In accordance with another embodiment, a computer-implemented method forreporting the presence of myocardial bridging in a patient includesdetecting, within a patient-specific model representing at least aportion of the patient's heart based on patient-specific anatomicalimage data regarding a geometry of the patient's heart, a segment of anepicardial coronary artery at least partially surrounded by thepatient's myocardium to determine the presence of myocardial bridging;and computing, using at least one computer processor, at least onephysical feature of the myocardial bridging to identify the severity ofthe myocardial bridging.

In accordance with another embodiment, a system for reporting thepresence of myocardial bridging in a patient includes a data storagedevice storing instructions for reporting the presence of myocardialbridging in a patient; and a processor configured to execute theinstructions to perform a method including: detecting, within apatient-specific model representing at least a portion of the patient'sheart based on patient-specific anatomical image data regarding ageometry of the patient's heart, a segment of an epicardial coronaryartery at least partially surrounded by the patient's myocardium todetermine the presence of myocardial bridging; and computing, using atleast one computer processor, at least one physical feature of themyocardial bridging to identify the severity of the myocardial bridging.

In accordance with another embodiment, a non-transitory computerreadable medium for use on at least one computer system containscomputer-executable programming instructions for performing a method forreporting the presence of myocardial bridging in a patient, the methodincludes: detecting, within a patient-specific model representing atleast a portion of the patient's heart based on patient-specificanatomical image data regarding a geometry of the patient's heart, asegment of an epicardial coronary artery at least partially surroundedby the patient's myocardium to determine the presence of myocardialbridging; and computing, using at least one computer processor, at leastone physical feature of the myocardial bridging to identify the severityof the myocardial bridging.

Additional embodiments and advantages will be set forth in part in thedescription which follows, and in part will be obvious from thedescription, or may be learned by practice of the disclosure. Theembodiments and advantages will be realized and attained by means of theelements and combinations particularly pointed out below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments and togetherwith the description, serve to explain the principles of the disclosure.

FIG. 1A is an exemplary diagram depicting a normal coronary artery,partial myocardial bridging, and full myocardial bridging;

FIG. 1B shows an exemplary patient's cCTA images showing myocardialbridging, with the arrows indicating a segment of a coronary arterytunneling into a patient's myocardium;

FIG. 2 is a schematic diagram of a system for providing variousinformation relating to coronary blood flow in a specific patient,according to an exemplary embodiment;

FIG. 3 is a flow chart of a method for reporting the presence ofmyocardial bridging, according to an exemplary embodiment;

FIG. 4 is a flow chart of a method for assessing the risk andhemodynamic significance of myocardial bridging, according to anexemplary embodiment;

FIG. 5 is a flow chart of a method for evaluating therapeutic optionsfor treating myocardial bridging, according to an exemplary embodiment;

FIG. 6 is a flow chart of a method for reporting the presence ofmyocardial bridging, according to an exemplary embodiment;

FIG. 7 is a flow chart of a method for assessing the risk andhemodynamic significance of myocardial bridging, according to anexemplary embodiment;

FIG. 8 is a flow chart of a method for evaluating therapeutic optionsfor myocardial bridging, according to an exemplary embodiment; and

FIG. 9 is a block diagram of an exemplary computer system in whichembodiments of the present disclosure may be implemented.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings. Whereverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

The present disclosure describes a non-invasive method to identify thepresence of myocardial bridging in patients and assess the functionalsignificance of myocardial bridging using a blood flow simulation undera simulated inotropic state of the myocardium. In doing so, the presentdisclosure provides a method to prescribe specific boundary conditionsby modeling the systolic compression of a coronary artery observed inthe tunneled segment. The methods described herein thus involveimprovements in the diagnosis of the physiologic consequences ofmyocardial bridging and the prediction of the potential benefits orrisks associated with alternate treatment strategies to correct thebridge or relieve ischemia.

The methods and systems disclosed herein use patient imaging to derive apatient-specific geometry of the blood vessels and myocardium, andcombine this geometry with the patient-specific physiologicalinformation and boundary conditions to perform blood flow simulation forpatients with myocardial bridging. For instance, the present disclosureprovides systems and methods for: (i) reporting the presence ofmyocardial bridging; (ii) assessing the risk and hemodynamicsignificance of myocardial bridging under different physiologic states;and (iii) evaluating therapeutic options for treating myocardialbridging. Non-limiting, exemplary methods and systems for each categoryare described herein. Moreover, the systems and methods disclosed hereinmay optionally comprise determining at least one blood flowcharacteristic, such as FFR, of the myocardial bridging segment duringsystole and/or diastole using patient-specific model representing atleast a portion of the patient's heart.

I. Report of Myocardial Bridging

FIG. 1A shows a normal coronary artery with no bridging, partialmyocardial bridging of a coronary artery, and full myocardial bridgingof a coronary artery. In FIG. 1A, “RV” indicates the right ventricle,“LV” indicates the left ventricle, and “S” indicates the septum. FIG. 1Bdepicts an exemplary patient's cCTA images showing myocardial bridging.The arrows point to a segment of a coronary artery encased in thepatient's myocardium. The left image is a cross-sectional view of thearea shown in the right image.

FIG. 3 shows aspects of a method 2000 for reporting the presence ofmyocardial bridging, according to an exemplary embodiment. Systems andmethods for reporting the presence of myocardial bridging may include,for example, obtaining patient-specific anatomical data, such as cardiacimages of the patient (step 2001). Patient-specific anatomical data maybe obtained non-invasively—e.g., via coronary computed tomographicangiography (cCTA) and/or magnetic resonance imaging (MRI). Moreover,patient-specific anatomical data may include data regarding the geometryof at least a portion of the patient's heart, e.g., at least a portionof the patient's aorta, a proximal portion of the main coronary arteries(and the branches extending therefrom) connected to the aorta, and themyocardium. Patients-specific anatomical data may be obtained via any ofthe methods described in U.S. Pat. No. 8,315,812, filed Jan. 25, 2011,and issued Nov. 20, 2012 (“the '812 patent”), the entire contents ofwhich are incorporated herein by reference.

Using the patient-specific anatomical data, method 2000 may compriseconstructing a patient-specific model of at least a portion of thepatient's heart, such as geometry of the patient's coronary arteries andthe patient's myocardium (step 2002). In at least one embodiment, thepatient-specific model may be chosen from a lumped-parameter model, aone-dimensional model, and a three-dimensional model. Patient-specificmodeling may be performed, for example, using any of the methodsdescribed in the '812 patent. FIG. 2 shows aspects of a system forproviding various information relating to coronary blood flow in aspecific patient, according to an exemplary embodiment. Athree-dimensional model 10 of the patient's anatomy may be created usingdata obtained noninvasively from the patient as will be described belowin more detail. Other patient-specific information may also be obtainednoninvasively. In an exemplary embodiment, the portion of the patient'sanatomy that is represented by the three-dimensional model 10 mayinclude at least a portion of the aorta and a proximal portion of themain coronary arteries (and the branches extending or emanatingtherefrom) connected to the aorta.

Various physiological laws or relationships 20 relating to coronaryblood flow may be deduced, e.g., from experimental data as will bedescribed below in more detail. Using the three-dimensional anatomicalmodel 10 and the deduced physiological laws 20, a plurality of equations30 relating to coronary blood flow may be determined as will bedescribed below in more detail. For example, the equations 30 may bedetermined and solved using any numerical method, e.g., finitedifference, finite volume, spectral, lattice Boltzmann, particle-based,level set, finite element methods, etc. The equations 30 may be solvableto determine information (e.g., pressure, velocity, FFR, etc.) about thecoronary blood flow in the patient's anatomy at various points in theanatomy represented by the model 10.

The equations 30 may be solved using a computer 40. Based on the solvedequations, the computer 40 may output one or more images or simulationsindicating information relating to the blood flow in the patient'sanatomy represented by the model 10. For example, the image(s) mayinclude a simulated blood pressure model 50, a simulated blood flow orvelocity model 52, a computed FFR (cFFR) model 54, etc., as will bedescribed in further detail below. The simulated blood pressure model50, the simulated blood flow model 52, and the cFFR model 54 provideinformation regarding the respective pressure, velocity, and cFFR atvarious locations along three dimensions in the patient's anatomyrepresented by the model 10. cFFR may be calculated as the ratio of theblood pressure at a particular location in the model 10 divided by theblood pressure in the aorta, e.g., at the inflow boundary of the model10, under conditions of increased coronary blood flow, e.g.,conventionally induced by intravenous administration of adenosine.

In an exemplary embodiment, the computer 40 may include one or morenon-transitory computer-readable storage devices that store instructionsthat, when executed by a processor, computer system, etc., may performany of the actions described herein for providing various informationrelating to blood flow in the patient. The computer 40 may include adesktop or portable computer, a workstation, a server, a personaldigital assistant, or any other computer system. The computer 40 mayinclude a processor, a read-only memory (ROM), a random access memory(RAM), an input/output (I/O) adapter for connecting peripheral devices(e.g., an input device, output device, storage device, etc.), a userinterface adapter for connecting input devices such as a keyboard, amouse, a touch screen, a voice input, and/or other devices, acommunications adapter for connecting the computer 40 to a network, adisplay adapter for connecting the computer 40 to a display, etc. Forexample, the display may be used to display the three-dimensional model10 and/or any images generated by solving the equations 30, such as thesimulated blood pressure model 50, the simulated blood flow model 52,and/or the cFFR model 54.

Method 2000 may also comprise detecting a segment of an epicardialcoronary artery at least partially surrounded by the patient'smyocardium, to determine the presence of myocardial bridging (step2003). In at least one embodiment, the epicardial coronary artery may bea major epicardial coronary artery. Step 2003 may include, for example,computing a signed distance map of the myocardial surface (inside:negative; outside: positive) and a signed distance map of the majorepicardial coronary arteries, using the coronary and myocardium modelfrom step 2002. Step 2003 may also include determining whether segmentsof epicardial coronary arteries belong to the negative distance map ofthe myocardial surface (i.e., inside the myocardium). If so, each suchsegment may be labeled as a myocardial bridging segment. Partialmyocardial bridging may be measured by the following metrics: proportionof the surface area of the coronary artery that is inside the myocardiumover the surface area of the total myocardial bridging segment; and/orproportion of the volume of the coronary artery that is inside themyocardium over the volume of the total myocardial bridging segment.

Method 2000 further may comprise reporting the presence of myocardialbridging and providing its severity by measuring one or more of thefollowing severity metrics: location of myocardial bridging (e.g., startand end distance to the ostium); length and/or depth of the tunneledsegment(s); eccentricity of the compressed segment(s) (such as insystolic image); and degree of systolic compression (when multiphaseimages are available) (step 2004). The severity of the myocardialbridging may be “scored” based on one or more of these severity metrics.For example, a higher myocardial bridging score may result from any ofthe following: more proximal myocardial bridging, deeper myocardialbridging, more eccentric myocardial bridging, and/or more systoliccompression. The proximity of the myocardial bridging may be measuredusing the length and/or depth of the myocardial bridging segment. Thedepth of the myocardial bridging may be measured based on theeccentricity of the myocardial bridging segment cross-section measuredby short axis length over long axis length. The eccentricity of themyocardial bridging, in turn, may be measured based on the degree ofsystolic compression (when multiphase images are available).

II. Assessment of Risk and Hemodynamic Significance of MyocardialBridging

FIG. 4 shows aspects of a method 3000 for assessing risk and thehemodynamic significance of myocardial bridging, according to anexemplary embodiment. Method 3000 may comprise obtainingpatient-specific physiologic information and boundary conditions (step3001). Patient-specific physiologic information and boundary conditionsmay be obtained, for example, using any of the methods described in the'812 patent.

The severity of myocardial bridging may be evaluated (step 3002), using,for example, one or more of the metrics described above with respect tostep 2004 of method 2000. Method 3000 may further comprise performingcomputational flow dynamics analysis under simulated hyperemic and/ordobutamine challenge conditions (e.g., increase in heart rate and/orsystolic BP) (step 3003).

Method 3000 also may comprise evaluating the hemodynamic significance ofone or more lesions associated with myocardial bridging by computing oneor more of volumetric flow rate, averaged pressure gradient over cardiaccycles, systolic and diastolic pressure gradients, fractional flowreserve (FFR), and diastolic Pd/Pa ratios (“Pd” is coronary pressuredistal to a myocardial bridging segment; “Pa” is coronary pressureproximal to a myocardial bridging segment) from simulation at themyocardial bridging segment (step 3004).

Optionally, method 3000 may further comprise evaluating the effect ofdynamic changes of one or more lesions due to cardiac contraction at themyocardial bridging segment by solving, for example, for blood flowthrough a deformable artery model subject to external compression(optional step 3005). For example, optional step 3005 may includesolving stress-equilibrium equations for a computational model of thecoronary artery and external myocardium structures using fluid-structureinteraction modeling techniques. The solution of structural mechanics ofthe coronary geometry in response to myocardial contraction may besolved iteratively along with the computational fluid dynamics (CFD).Alternatively, the fluid-structure interaction equations may be solvedin a coupled manner using an arbitrary Lagrangian-Eulerian framework.

III. Evaluation of Therapeutic Options for Myocardial Bridging

FIG. 5 shows aspects of a method 4000 for evaluating therapeutic optionsfor treating myocardial bridging, according to an exemplary embodiment.Method 4000 may comprise evaluating the effectiveness of pharmacologictherapy on myocardial bridging (step 4001). Method 4000 may also oralternatively comprise evaluating the effectiveness of percutaneouscoronary intervention (PCI) using virtual PCI (step 4002). Method 4000may further or alternatively comprise evaluating the effectiveness ofcoronary artery bypass grafting surgery (CABG) (step 4003). Method 4000additionally or alternatively may comprise evaluating the effect ofsurgical correction of the bridge, i.e., removing tissue over thebridge, which may be termed “unroofing” (step 4004). Method 4000 also oralternatively may comprise suggesting an optimal treatment protocol(step 4005).

IV. Exemplary Embodiment

A non-limiting exemplary embodiment of a method and system for reportinga myocardial bridge and patient impact is provided below.

Reporting the Presence of Myocardial Bridging

FIG. 6 shows additional aspects of method 2000 for reporting thepresence of myocardial bridging, according to an exemplary embodiment.Method 2000 may comprise, for example, acquiring, for one or morepatients, a digital representation (e.g., the memory or digital storage[e.g., hard drive, network drive] of a computational device such as acomputer, laptop, DSP, server, etc.) of a patient-specific model of thegeometry for at least a portion of the patient's heart, such as thepatient's ascending aorta, coronary artery tree, and myocardium (step2002). This geometry may be represented as a list of points in space(possibly with a list of neighbors for each point) in which the spacecan be mapped to spatial units between points (e.g., millimeters), forexample. This model may be derived, for example, by performing a cardiaccomputerized tomography (CT) scan in the end diastole phase of thecardiac cycle or using Magnetic Resonance Imaging (MRI). The image(s)may be segmented manually or automatically to identify voxels belongingto the lumen of the coronary arteries and myocardium. Inaccuracies inthe geometry extracted automatically may be corrected by a humanobserver who compares the extracted geometry with the images and makescorrections as needed. Once the voxels are identified, the geometricmodel can be extracted (e.g., using marching cubes techniques). In atleast one embodiment, the patient-specific model may be chosen from alumped-parameter model, a one-dimensional model, and a three-dimensionalmodel.

Using the constructed coronary and myocardium patient-specific model,method 2000 may comprise automatically detecting a segment of aepicardial coronary artery surrounded by the myocardium as exhibitingmyocardial bridging (step 2003) and computing one or more features ofmyocardial bridging (step 2004). In at least one exemplary embodiment,the epicardial coronary artery may be a major epicardial coronaryartery.

In at least one exemplary embodiment, step 2004 comprises computing oneor more features of the detected myocardial bridging (step 2004A) andfurther computing one or more myocardial bridging metrics (step 2004B).Step 2004A, for example, may comprise computing a signed distance map ofa myocardial surface, wherein the inside is negative and the outside ispositive. Step 2004A may also comprise computing a signed distance mapof one or more epicardial coronary arteries. In at least one embodiment,the one or more epicardial coronary arteries may be one or more majorepicardial coronary arteries. Step 2004A may further comprisedetermining one or more segments of epicardial coronary arteries thatbelong to the negative distance map of the myocardial surface (i.e.,inside the myocardium).

Step 2004B may comprise computing one or more of the followingmyocardial bridging metrics: the location of the myocardial bridgingsegment (e.g., start and end distance to the ostium); the length of themyocardial bridging segment; the depth of the myocardial bridgingsegment; the eccentricity of a cross-section of the myocardial bridgingsegment, which may be measured, for example, by short axis length overlong axis length; and the degree of systolic compression (whenmultiphase images are available).

Method 2000 may further comprise storing the results of the computedpresence and features of myocardial bridging with images (step 2004C).For example, the results may be saved as a digital representation (e.g.,the memory or digital storage [e.g., hard drive, network drive] of acomputational device such as a computer, laptop, DSP, server, etc.).Step 2004C may also comprise transmitting or making the resultsavailable to a health care provider, such as a physician.

Assessment of Risk and Hemodynamic Significance of Myocardial Bridging

FIG. 7 shows additional aspects of method 3000 for assessing the riskand hemodynamic significance of myocardial bridging, according to anexemplary embodiment. The methods and systems described herein mayemploy, for example, computational fluid dynamics, fluid-structureinteraction analysis, and/or machine-learning based estimations ofsystolic compression of coronary and physiologic boundary conditions tosimulate hyperemic coronary flow and the inotropic state of themyocardium.

Method 3000 may comprise, for example, acquiring, for one or morepatients, a digital representation (e.g., the memory or digital storage[e.g., hard drive, network drive] of a computational device such as acomputer, laptop, DSP, server, etc.) of: (a) a patient-specific model ofthe geometry of at least a portion of the patient's heart, such as thepatient's ascending aorta, coronary artery tree, and myocardium, foreach time point and (b) a list of estimates of physiological orphenotypic parameters of the patient for each time point (step 3001A).

The geometry of at least a portion of the patient's heart, such as thepatient's ascending aorta, coronary artery tree, and myocardium, may berepresented as a list of points in space (possibly with a list ofneighbors for each point, for example) in which the space can be mappedto spatial units between points (e.g., millimeters). Thepatient-specific model may be derived by performing a cardiac CT imagingof the patient in the end diastole phase of the cardiac cycle. The imagethen may be segmented manually or automatically to identify voxelsbelonging to the aorta and the lumen of the coronary arteries. Given a3-D image of coronary vasculature, any method for extracting apatient-specific model of cardiovascular geometry may be used,including, for example, any of the methods described in the '812 patent.Inaccuracies in the geometry extracted automatically may be corrected bya human observer who compares the extracted geometry with the images andmakes corrections as needed. Once the voxels are identified, thegeometric model can be derived (e.g., using marching cubes). In at leastone embodiment, the patient-specific model may be chosen from alumped-parameter model, a one-dimensional model, and a three-dimensionalmodel.

As non-limiting, exemplary examples, the list of estimates ofphysiological or phenotypic parameters of the patient may include one ormore of: blood pressure; resting heart rate; hematocrit level; patientage and/or gender; myocardial mass, e.g., as derived by segmenting themyocardium in the image, calculating the volume in the image, and usingan estimated density of 1.05 g/mL to estimate the myocardial mass; andgeneral risk factors of coronary artery disease (e.g., smoking,diabetes, family history, weight, etc.).

Method 3000 may also comprise defining at least one physiologiccondition and/or at least one boundary condition of the patient in aphysiologic stress state (step 3001B). This stress state may be, forexample, either one induced during a diagnostic test, e.g. usingdobutamine, or simulated, e.g., using mild, moderate, or intenseexercise. The at least one physiologic condition and/or at least oneboundary condition of the patient under hyperemic conditions may bedefined using the methods described in the '812 patent. Alternatively,the at least one physiologic condition and/or at least one boundarycondition of the patient after treatment may be defined using themethods described in U.S. Pat. No. 8,249,815, filed Nov. 7, 2011, issuedAug. 21, 2012, the contents of which are incorporated herein byreference. The at least one physiologic condition and/or at least oneboundary condition of the patient under an inotropic condition of themyocardium may be defined using the effect of dobutamine on thepatient's physiology, such as an increase in heart rate and/or anincrease in systolic pressure.

Method 3000 may comprise determining or estimating systolic compressionof a coronary artery (step 3002). Step 3002 may be accomplished, forexample, using multiphase (e.g., diastole, systole) images to measurethe degree of systolic compression of tunneled segments. For instance,the degree of systolic compression may be measured by computingcross-sectional areas of myocardial bridging segments of the coronarysurface meshes derived from the multiphase images. As anothernon-limiting example, step 3002 may be accomplished using literaturedata to estimate the systolic compression of a coronary artery at thetunneled segments at rest. For instance, literature data may teach a71%+/−16% reduction of diameter within myocardial bridging as comparedto a proximal/distal segment in systole, and a 35%+/−13% reduction ofdiameter in diastole. The patient geometry thus may be perturbed suchthat the myocardial bridging segments have a 71% reduction in diameterin systole and a 35% reduction in diastole. Specifically, the meshcoordinates of an identified myocardial bridging segment may betransformed in the perpendicular direction of the centerline with theproportions of 71%+/−16% and 35%+/−13% for systolic and diastolicphases, respectively.

Step 3002 may also be accomplished using machine-learning based methodsto estimate the systolic compression of a coronary artery at tunneledsegments from noninvasive images acquired at rest. Examples of machinelearning methods are described, for example, in U.S. patent applicationSer. No. 13/895,893, filed May 16, 2013, Ser. No. 13/895,871, filed May16, 2013, and Ser. No. 14/011,151, filed Aug. 27, 2013, the contents ofall of which are incorporated herein by reference.

An exemplary embodiment of a machine learning method is disclosedherein. The machine learning method may comprise a training mode and aprediction mode. In a training mode, the machine learning method maycomprise creating a feature vector of the bridged artery from cCTA data.An exemplary feature vector may contain, for example: age, sex, heartrate, systolic and diastolic pressure, and/or epicardial fat volume;myocardial mass, regional density of myocardium, ejection fraction,and/or myocardial contractility; depth of coronary in relation toepicardium surface; and/or the length of the bridged segment. Themachine learning method also may comprise associating this featurevector with invasive measurements of the bridged segment from imagingmethods, such as angiography, intravascular ultrasound (IVUS), andoptical coherence tomography (OCT). Non-limiting examples of invasivemeasurements include the depth and length of the bridged artery and thedegree of systolic compression measured by change in diameter, area, andeccentricity of cross-sectional lumen. The machine learning method alsomay comprise training a machine learning algorithm (e.g., a linearSupport Vector Machine) to the degree of systolic compression from thefeature vectors obtained. The machine learning method further maycomprise saving the results of the machine learning algorithm as adigital representation (e.g., memory or digital storage [e.g., harddrive, network drive] of a computational device such as a computer,laptop, DSP, server, etc.).

In the prediction mode, the machine learning method may comprisecreating a feature vector of the bridged artery from cCTA data. Thefeature vector may be the same as the quantities used in the trainingmode. The machine learning method also may comprise using the savedresults of the machine learning algorithm produced in the training mode(e.g., feature weights) to produce estimates of the degree of systoliccompression. These estimates may be produced using the same machinelearning technique used in the training mode. The machine learningmethod may comprise saving the estimated degree of systolic compressionto a digital representation (e.g., memory or digital storage [e.g., harddrive, network drive] of a computational device such as a compute,laptop, DSP, server, etc.). The machine learning method further maycomprise using the predicted degree of compression for a fluid-structureinteraction simulation or reporting it to a health care provider, suchas a physician.

Method 3000 may also comprise performing computational fluid dynamicsanalysis and/or structural mechanics simulation. This analysis may beperformed using, for example, a three-dimensional finite element, finitevolume, lattice Boltzman, level set, particle based method to solve thefull equations of blood flow and pressure, and/or a fluid-structureinteraction method to solve for at least one of blood flow, pressure andvessel wall motion, and deformation of the bridged segment (step 3003).

Method 3000 further may comprise computing at least one hemodynamicquantity of the myocardial bridging segment. For example, method 300 maycomprise computing at least one of blood flow rate, averaged pressuregradient over cardiac cycles, systolic and diastolic pressure gradients,FFR over an entire cardiac cycle, and Pd/Pa over diastolic phase (step3004). Step 3004 may also comprise computing wall shear stress at themyocardial bridging segment and/or the pressure gradient at themyocardial bridging segment.

Method 3000 may also comprise storing the results of the computed atleast one hemodynamic quantity representing the risk of myocardialbridging with images (step 3006). For example, the results may be savedas a digital representation (e.g., the memory or digital storage [e.g.,hard drive, network drive] of a computational device such as a computer,laptop, DSP, server, etc.). Step 3006 may further comprise transmittingor making the results available to a health care provider, such as aphysician.

Evaluation of Therapeutic Options for Myocardial Bridging

FIG. 8 shows additional aspects of method 4000 for evaluatingtherapeutic options for myocardial bridging, according to an exemplaryembodiment. The methods and systems for guiding treatment optionsdisclosed herein may use a machine-learning based risk predictorestablished in previous steps by evaluating several therapeutic optionsfor myocardial bridging.

In at least one embodiment, method 4000 may comprise evaluating theeffectiveness or risk of pharmacologic therapy (e.g. beta-blockers,calcium channel blockers, and/or nitrates) on myocardial bridging (step4001). For example, step 4001 may comprise iteratively updating thepatient-specific model to simulate use of one or more pharmacologictherapies and evaluating the results. Modeling one or more pharmacologictherapies may be accomplished, for example, using any of the methodsdescribed in the '812 patent.

Method 4000 may also comprise evaluating the effectiveness of PCI usingvirtual PCI (step 4002). For example, step 4002 may comprise updatingthe patient-specific model to simulate insertion of one or more stentsand evaluating the results. Modeling insertion of one or more stents maybe accomplished, for example, using any of the methods described in the'812 patent.

In addition, method 4000 may comprise evaluating the effectiveness ofcoronary artery bypass grafting surgery (CABG) (step 4003). For example,step 4003 may comprise updating the patient-specific model to simulatesurgically inserting at least one bypass and evaluating the results.Modeling bypass grafting surgery may be accomplished, for example, usingany of the methods described in the '812 patent.

Method 4000 may further comprise evaluating the effectiveness ofsurgical unroofing of the bridged segment (step 4004). The effect ofsurgical unroofing of the bridged segment can be realized by lesseningthe external compression of the artery on the epicardial side of thecoronary artery while maintaining the compressive force on theendocardial side and lateral walls. For example, this effect can bemodeled by prescribing external compression varying around thecircumference of the artery along the length of the bridged segment.Method 4000 may also comprise suggesting an optimal treatment protocol(step 4005).

FIG. 9 provides a high-level functional block diagram illustrating anexemplary general-purpose computer 7000. Computer 7000 may be used toimplement, for example, any of the methods described above. It isbelieved that those skilled in the art are familiar with the structure,programming, and general operation of such computer equipment and as aresult, the drawings should be self-explanatory.

In an example, computer 7000 may represent a computer hardware platformfor a server or the like. Accordingly, computer 7000 may include, forexample, a data communication interface for packet data communication7600. The platform may also include a central processing unit (CPU)7200, in the form of one or more processors, for executing programinstructions. The platform typically includes an internal communicationbus 7100, program storage, and data storage for various data files to beprocessed and/or communicated by the platform such as ROM 7300 and RAM7400, although the computer 7000 often receives programming and data vianetwork communications 7700. The hardware elements, operating systems,and programming languages of such equipment are conventional in nature,and it is presumed that those skilled in the art are adequately familiartherewith. Computer 7000 also may include input and output ports 7500 toconnect with input and output devices such as keyboards, mice,touchscreens, monitors, displays, etc. Of course, the various serverfunctions may be implemented in a distributed fashion on a number ofsimilar platforms, to distribute the processing load. Alternatively, theservers may be implemented by appropriate programming of one computerhardware platform.

Any aspect set forth in any embodiment may be used with any otherembodiment set forth herein. Every device and apparatus set forth hereinmay be used in any suitable medical procedure, may be advanced throughany suitable body lumen and body cavity, and may be used for imaging anysuitable body portion.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the disclosed systems andprocesses without departing from the scope of the disclosure. Otherembodiments will be apparent to those skilled in the art fromconsideration of the specification and practice of the disclosuredisclosed herein. It is intended that the specification and examples beconsidered as exemplary only, with a true scope and spirit of thedisclosure being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method for determining orpredicting systolic compression of a patient, the method comprising:receiving one or more patient-specific images of a patient's heart;computationally generating a patient-specific model using the one ormore received patient-specific images, the patient-specific modelrepresenting at least a portion of the patient's myocardium and anepicardial coronary artery of the patient; computationally detecting, inthe patient-specific model, a segment of the epicardial coronary arterythat is inside, at least partially surrounded by, or adjacent to asurface of the modeled myocardium; computing systolic compression of thesegment of the epicardial coronary artery that is inside, at leastpartially surrounded by, or adjacent to a surface of the modeledmyocardium; determining one or more patient-related hemodynamiccharacteristics associated with the patient; determining and storing anassociation between the computed systolic compression of the segment andthe one or more patient-related hemodynamic characteristics; receivingone or more hemodynamic characteristics associated with an individual;determining a systolic compression associated with the individual'scoronary artery, based on the one or more received hemodynamiccharacteristics associated with the individual and the associationbetween the computed systolic compression and the one or morepatient-related hemodynamic characteristics.
 2. The method of claim 1,further comprising: determining a value of at least one hemodynamiccharacteristic based on the determined systolic compression associatedwith the individual's coronary artery.
 3. The method of claim 2, whereinthe at least one hemodynamic characteristic includes blood flow rate,blood pressure, blood pressure gradient over cardiac cycles, systolicand diastolic pressure gradients, fractional flow reserve, Pd/Pa, vesselwall motion, and/or vessel deformation.
 4. The method of claim 2,further comprising: determining a risk of myocardial bridging or ahemodynamic significance of myocardial bridging associated with thedetermined value of the hemodynamic characteristic.
 5. The method ofclaim 1, wherein the one or more hemodynamic characteristics associatedwith the patient includes age, sex, heart rate, systolic pressure,diastolic pressure, epicardial fat volume, myocardial mass, regionaldensity of myocardium, ejection fraction, myocardial contractility,depth of coronary in relation to epicardium surface, length of thebridged segment, depth and length of a bridged artery, degree ofsystolic compression, diameter of a vessel lumen, area of a vessellumen, and/or eccentricity of a cross-section of a vessel lumen.
 6. Themethod of claim 1, further comprising: determining a treatmentrecommendation for the individual based on the determined systoliccompression associated with the individual's coronary artery.
 7. Themethod of claim 1, wherein the one or more patient images include aplurality of multiphase images.
 8. The method of claim 1, wherein thepatient-specific model is a lumped-parameter model, a one-dimensionalmodel, or a three-dimensional model.
 9. A system for determining orpredicting systolic compression of a patient, the system comprising: adata storage device storing instructions for determining or predictingsystolic compression of the patient; and a processor configured toexecute the instructions to perform a method including: receiving one ormore patient-specific images of a patient's heart; computationallygenerating a patient-specific model using the one or more receivedpatient-specific images, the patient-specific model representing atleast a portion of the patient's myocardium and an epicardial coronaryartery of the patient; computationally detecting, in thepatient-specific model, a segment of the epicardial coronary artery thatis inside, at least partially surrounded by, or adjacent to a surface ofthe modeled myocardium; computing systolic compression of the segment ofthe epicardial coronary artery that is inside, at least partiallysurrounded by, or adjacent to a surface of the modeled myocardium;determining one or more patient-related hemodynamic characteristicsassociated with the patient; determining and storing an associationbetween the computed systolic compression of the segment and the one ormore patient-related hemodynamic characteristics; receiving one or morehemodynamic characteristics associated with an individual; determining asystolic compression associated with the individual's coronary artery,based on the one or more received hemodynamic characteristics associatedwith the individual and the association between the computed systoliccompression and the one or more patient-related hemodynamiccharacteristics.
 10. The system of claim 9, wherein the system isfurther configured for: determining a value of at least one hemodynamiccharacteristic based on the determined systolic compression associatedwith the individual's coronary artery.
 11. The system of claim 10,wherein the at least one hemodynamic characteristic includes blood flowrate, blood pressure, blood pressure gradient over cardiac cycles,systolic and diastolic pressure gradients, fractional flow reserve,Pd/Pa, vessel wall motion, and/or vessel deformation.
 12. The system ofclaim 10, wherein the system is further configured for: determining arisk of myocardial bridging or a hemodynamic significance of myocardialbridging associated with the determined value of the hemodynamiccharacteristic.
 13. The system of claim 9, wherein the one or morehemodynamic characteristics associated with the patient includes age,sex, heart rate, systolic pressure, diastolic pressure, epicardial fatvolume, myocardial mass, regional density of myocardium, ejectionfraction, myocardial contractility, depth of coronary in relation toepicardium surface, length of the bridged segment, depth and length of abridged artery, degree of systolic compression, diameter of a vessellumen, area of a vessel lumen, and/or eccentricity of a cross-section ofa vessel lumen.
 14. The system of claim 9, wherein the system is furtherconfigured for: determining a treatment recommendation for theindividual based on the determined systolic compression associated withthe individual's coronary artery.
 15. The system of claim 9, wherein theone or more patient images include a plurality of multiphase images. 16.The system of claim 9, wherein the patient-specific model is alumped-parameter model, a one-dimensional model, or a three-dimensionalmodel.
 17. A non-transitory computer readable medium for use on at leastone computer system containing computer-executable programminginstructions for performing a method for determining or predictingsystolic compression, the method comprising: receiving one or morepatient-specific images of a patient's heart; computationally generatinga patient-specific model using the one or more received patient-specificimages, the patient-specific model representing at least a portion ofthe patient's myocardium and an epicardial coronary artery of thepatient; computationally detecting, in the patient-specific model, asegment of the epicardial coronary artery that is inside, at leastpartially surrounded by, or adjacent to a surface of the modeledmyocardium; computing systolic compression of the segment of theepicardial coronary artery that is inside, at least partially surroundedby, or adjacent to a surface of the modeled myocardium; determining oneor more patient-related hemodynamic characteristics associated with thepatient; determining and storing an association between the computedsystolic compression of the segment and the one or more patient-relatedhemodynamic characteristics; receiving one or more hemodynamiccharacteristics associated with an individual; determining a systoliccompression associated with the individual's coronary artery, based onthe one or more received hemodynamic characteristics associated with theindividual and the association between the computed systolic compressionand the one or more patient-related hemodynamic characteristics.
 18. Thenon-transitory computer readable medium of claim 17, the method furthercomprising: determining a value of at least one hemodynamiccharacteristic based on the determined systolic compression associatedwith the individual's coronary artery.
 19. The non-transitory computerreadable medium of claim 18, wherein the at least one hemodynamiccharacteristic includes blood flow rate, blood pressure, blood pressuregradient over cardiac cycles, systolic and diastolic pressure gradients,fractional flow reserve, Pd/Pa, vessel wall motion, and/or vesseldeformation.
 20. The non-transitory computer readable medium of claim18, the method further comprising: determining a risk of myocardialbridging or a hemodynamic significance of myocardial bridging associatedwith the determined value of the hemodynamic characteristic.